fracnetics 1.0.1

Genetic Network Programming with Fractal Expansion

Fracnetics

Fracnetics is a high-performance C++ library with Python bindings for Genetic Network Programming (GNP) — an evolutionary approach that evolves directed graph structures for decision-making and control tasks.

Bridging evolution, networks, and fractals into one framework.

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🔬 Overview

Fracnetics extends Genetic Network Programming (GNP) with variable-size networks and fractal geometry, designed to model adaptive, recursive, and self-similar network structures inspired by biological evolution and natural growth patterns.

It is designed for researchers and developers exploring:

• Evolutionary network design
• Obtain fully interpretable networks
• Training agents in dynamic environments
• Directed multigraph network architecture
• variable-size networks based on Fabian Köhnke and Christian Borgelt, 2025
• Complex systems with self-similar topology

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🚀 Installation

pip install fracnetics

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📓 Colab Tutorials

Small Tutorials Using Fracnetics

This notebooks demonstrate how to use the Fracnetics library to solve the CartPole environment problem from Gymnasium (a fork of OpenAI Gym) and the simple Irisdata classification.

CartPole Tutorial

Iris Tutorial

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🦾 Features

• Population Management

  • Create and manage a full population of Network individuals.
  • Graph-Based Evolution: Networks evolve as directed graphs with judgment nodes (conditional decisions) and processing nodes (actions), enabling node reuse and cyclic execution paths

• Fitness Evaluation

  • Built-in support for OpenAI Gymnasium environments
  • Traverse a network path via callTraversePath and calculate any own fitness/ target function

• Selection & Elitism: Tournament selection with configurable size.

• Mutation Operators

  • Edge mutation (altering connections between nodes).
  • Boundary mutations (multiple variants):
    • Uniform
    • Normally distributed
    • Network-size dependent sigma scaling
    • Edge-count dependent sigma scaling
    • Fractal-based mutation

• Crossover: Node exchange between individuals.

• Innovative Opreators

  • Add/Delete Nodes – dynamic structural changes in networks.
  • Fractal Geometry Integration – hierarchical boundary generation via production rules (L-systems-style subdivision).

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